Evolutionary pattern search algorithms for unconstrained and linearly constrained optimization

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The authors describe a convergence theory for evolutionary pattern search algorithms (EPSAs) on a broad class of unconstrained and linearly constrained problems. EPSAs adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSAs is inspired by recent analyses of pattern search methods. The analysis significantly extends the previous convergence theory for EPSAs. The analysis applies to a broader class of EPSAs,and it applies to problems that are nonsmooth, have unbounded objective functions, and which are linearly constrained. Further, they describe a modest change to the algorithmic framework of ... continued below

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23 p.

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HART,WILLIAM E. June 1, 2000.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM, and Livermore, CA (United States)
    Place of Publication: Albuquerque, New Mexico

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Description

The authors describe a convergence theory for evolutionary pattern search algorithms (EPSAs) on a broad class of unconstrained and linearly constrained problems. EPSAs adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSAs is inspired by recent analyses of pattern search methods. The analysis significantly extends the previous convergence theory for EPSAs. The analysis applies to a broader class of EPSAs,and it applies to problems that are nonsmooth, have unbounded objective functions, and which are linearly constrained. Further, they describe a modest change to the algorithmic framework of EPSAs for which a non-probabilistic convergence theory applies. These analyses are also noteworthy because they are considerably simpler than previous analyses of EPSAs.

Physical Description

23 p.

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OSTI as DE00756064

Medium: P; Size: 23 pages

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  • Journal Name: IEEE Transactions on Evolutionary Computation; Other Information: Submitted to IEEE Transactions on Evolutionary Computation

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  • Report No.: SAND2000-1395J
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 756064
  • Archival Resource Key: ark:/67531/metadc709901

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  • June 1, 2000

Added to The UNT Digital Library

  • Sept. 12, 2015, 6:31 a.m.

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  • April 10, 2017, 3:45 p.m.

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HART,WILLIAM E. Evolutionary pattern search algorithms for unconstrained and linearly constrained optimization, article, June 1, 2000; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc709901/: accessed June 23, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.